a data assimilation approach to quantify uncertainty for estimates of biomass stocks and changes in...
TRANSCRIPT
A data assimilation approach to quantify uncertainty for estimates of biomass stocks and
changes in Amazon forests
Paul DuffyMichael KellerDoug Morton
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Outline
• Consider the generation of data products based on inventory and lidar data
• Initial results for the combination of information from these data products
• Discuss next steps for additional uncertainty quantification
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Approach
• Generate low level Aboveground Carbon Density (Mg C ha-1) data products based on both inventory and lidar data
• Implement a statistical data assimilation algorithm to generate spatially explicit estimates of higher order data products with uncertainty
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Approach
• Use a Hierarchical modeling frame work– Data models (lidar and inventory)– Process models (Land Use, Topography)– Parameter models (measurement error, spatial
range, etc.)
Inventory Data
• 22 transects of 20x500m were measured
• Biomass for each tree was estimated
• E.g. 0.051*specific density*DBH^2*Total height (Chave 2005)
Lidar Data
The variation within the corresponding CHM
pixel is depicted by this distribution
Lidar P100 Returns Heights with Transects
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Data Model: Measurement Error
• Lidar and inventory are considered as distinct and uncertain measurements of the unobservable ACD
• Specific sources of uncertainty can be due to:– Sampling error, allometry models– Lidar data acquisition strategy– Spatial resolution (25m2, 50m2, etc.)
Lidar Within Pixel SD for Returns Heights
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Process Model Development
• At spatial scales corresponding to the size of the domain for our analysis, land use is the strongest driver
• Currently, the deterministic component of our process model is just a mean term
• We will use satellite imagery to build land cover explanatory variables for potential use in the process model
Lidar Variograms for P100 Returns Heights
0 50 100 150 200
0400
800
10m
Distance
Semi-Variance
0 50 100 150 200
0400
800
25m
Distance
Semi-Variance
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Assimilation for High–Level Data Products
• Preliminary implementation of assimilation algorithms
• Quantitative measures of uncertainty associated with high-level data products can be the endpoint for characterization
Assimilation for a test Subregion
Mean of Assimilated ACD Data Product (Mg C ha-1)
Standard Deviation of Assimilated ACD Data Product (Mg C ha-1)
Estimated Standard Deviation of Assimilated Data Product
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Limitations
• Current approach utilizes uncertainty reducing assumptions
– Lidar component regression of Aboveground Carbon Density ~ height
– Inventory component regression of Aboveground biomass ~ height, dbh, wsd
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Next Steps
• Account for uncertainty in the parameters in the allometric models
• Use analyses of LandSat time series to characterize disturbance
• Expand from the test region to the full Municipality of Paragominas
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Acknowledgement
• Data were acquired by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, and USAID, and the US Department of State.